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Urine metabolomics showed distinct patterns across paediatric TB states

A medical professional wearing blue gloves and safety glasses operates a binocular microscope in a laboratory setting, with petri dishes containing colored samples visible in the foreground and laboratory equipment in the background.
Research area:Biochemistry, Genetics and Molecular BiologyMetabolomicsUrine

What the study found

Urine high-resolution 1H-nuclear magnetic resonance (HR-1H-NMR) metabolomics showed different patterns across childhood tuberculosis (TB) disease, TB infection (TBI), and healthy controls.

Why the authors say this matters

The authors say early diagnosis of TB in children is difficult because symptoms are not specific, bacterial loads are low, and respiratory samples are hard to obtain. They suggest urine metabolomics could be a non-invasive way to help prioritize children for microbiological testing and may eventually reduce the need for invasive sampling.

What the researchers tested

The researchers retrospectively analysed urine samples from 101 children in the Spanish Paediatric TB Network: 62 with TB, 17 with TBI, and 22 healthy controls. They generated metabolic fingerprints with 1H-NMR spectroscopy and assessed group separation with partial least squares discriminant analysis (PLS-DA) using cross-validation; metabolites were selected using VIP > 2 and p < 0.05.

What worked and what didn't

Validated PLS-DA models for TB vs. healthy controls, microbiologically confirmed TB vs. healthy controls, and microbiologically confirmed TB vs. TBI reached at least 70% accuracy, with AUC-ROC values from 0.867 to 0.971. The strongest separation was for microbiologically confirmed TB vs. TBI (AUC-ROC 0.971; sensitivity 90.0%; specificity 86.7%), while the authors state the approach is not yet suitable as a confirmatory test.

What to keep in mind

This was a retrospective analysis of a relatively small sample from one network. The abstract describes preliminary findings and does not report external validation beyond the cross-validated models.

Key points

  • Urine HR-1H-NMR metabolomics showed distinct signatures across paediatric TB disease, TB infection, and healthy controls.
  • The strongest model separation was for microbiologically confirmed TB versus TB infection (AUC-ROC 0.971).
  • TB disease was associated with higher phenylalanine and two unidentified NMR signals, plus lower levels of several energy- and nitrogen-related metabolites.
  • TB infection was associated with higher isoleucine, N-acetylglutamine, glutamine, creatinine, and 2-hydroxyvalerate.
  • The authors say the approach is not yet suitable as a confirmatory test but may be useful for triage.

Disclosure

Research title:
Urine metabolomics showed distinct patterns across paediatric TB states
Authors:
Andrea López-Suárez, Patricia Comella-del-Barrio, Pilar Alonso-Moreno, Yaiza Marín, Palmira Villa, Mar Santos, Antoni Noguera-Julian, Anna Gamell, Paula Rodríguez-Molino, Jara Hurtado-Gallego, Daniel Blázquez-Gamero, Ana Belén Jiménez-Jiménez, Adriana Navas, Elena del Castillo, Marta Llorente, Talía Sainz, José Domínguez, José Luis Izquierdo-García, Begoña Santiago-García
Institutions:
Universidad Complutense de Madrid, Instituto de Salud Carlos III, Hospital General Universitario Gregorio Marañón, Centre for Biomedical Network Research on Rare Diseases, Medical Research Network, Departament de Salut, Red de Investigación en Actividades Preventivas y Promoción de la Salud, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Universitat de Barcelona, Hospital Universitario La Paz, Hospital La Paz Institute for Health Research, Research Institute Hospital 12 de Octubre, Hospital Universitario 12 De Octubre, Hospital Universitario Fundación Jiménez Díaz, Hospital Universitario Infanta Leonor, Centro de Implantología Cirugía Oral y Maxilofacial, Spanish Society of Hematology and Hemotherapy, Universidad Autónoma de Madrid, Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol
Publication date:
2026-02-23
DOI:
10.1038/s41598-026-40619-y
OpenAlex record:
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AI provenance: This post was generated by OpenAI. The original authors did not write or review this post.

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